arXiv 2407.093672024-07-18cs.CV

重塑持续测试时适应的在线数据缓冲与组织机制

Reshaping the Online Data Buffering and Organizing Mechanism for Continual Test-Time Adaptation

Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）；Shenzhen University of Advanced Technology（深圳先进技术学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Science（中国科学院深圳先进技术研究院）

Zhilin Zhu, Xiaopeng Hong, Zhiheng Ma, Weijun Zhuang, Yaohui Ma, Yong Dai, Yaowei Wang

备注：: This is the preprint version of our paper and supplemental material to appear in ECCV 2024

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AI总结：

针对持续测试时适应中的在线、无监督及错误累积和灾难性遗忘挑战，提出不确定性感知缓冲、图类关系保持约束和伪目标回放机制，在分割与分类任务中验证了优越性。

摘要：

持续测试时适应（CTTA）涉及将预训练的源模型适应到持续变化的无监督目标域。本文系统分析了该任务的挑战：在线环境、无监督性质，以及在持续域偏移下错误累积和灾难性遗忘的风险。为应对这些挑战，我们重塑了CTTA的在线数据缓冲与组织机制。我们提出了一种基于不确定性的缓冲方法，从无监督的单遍数据流中识别并聚合具有高确定性的重要样本。在此基础上，我们提出了一种基于图的类关系保持约束来克服灾难性遗忘。此外，使用伪目标回放目标来缓解错误累积。大量实验证明了我们的方法在分割和分类CTTA任务中的优越性。代码可在https://github.com/z1358/OBAO获取。

英文摘要：

Continual Test-Time Adaptation (CTTA) involves adapting a pre-trained source model to continually changing unsupervised target domains. In this paper, we systematically analyze the challenges of this task: online environment, unsupervised nature, and the risks of error accumulation and catastrophic forgetting under continual domain shifts. To address these challenges, we reshape the online data buffering and organizing mechanism for CTTA. We propose an uncertainty-aware buffering approach to identify and aggregate significant samples with high certainty from the unsupervised, single-pass data stream. Based on this, we propose a graph-based class relation preservation constraint to overcome catastrophic forgetting. Furthermore, a pseudo-target replay objective is used to mitigate error accumulation. Extensive experiments demonstrate the superiority of our method in both segmentation and classification CTTA tasks. Code is available at https://github.com/z1358/OBAO.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。